Semiconductor device manufacturing continues to become more complex as demands of the marketplace drive increasing circuit density. For example, a typical manufacturing process entails starting with a slice of single crystal silicon (referred to herein as a wafer or a substrate), and processing it through a series of from about 300 to about 500 individual manufacturing steps to produce many individual devices on each wafer. In addition, as circuit density increases, line widths shrink, and newer technology and materials are being used in semiconductor manufacturing. For example, copper processes are migrating from a 0.18 μm technology to a 0.13 μm technology. In addition, the advent of copper seed and electroplating, along with added difficulties in using new low-k films and the need to fill 0.13 um structures, present hurdles made no easier by a transition to use of 300 mm wafers.
As a result of this, reducing defectivity, be it process or contamination induced, is becoming increasingly more important, and one important factor in overcoming defectivity is time-to-root-cause. To monitor defectivity, and continuously reduce defect densities, semiconductor device manufacturers use defect data management software and defect detection equipment. Further, each new generation of semiconductor process technology demands more detailed data to support process monitoring and improvement. In turn, the wealth of data drives data storage, data integration, data analysis, and data automation trends in wafer processing.
Within the last few years, a good deal of investment has gone into the deployment of data extraction systems designed to record operating conditions of a given semiconductor wafer processing tool during the time a wafer is being processed. Although this temporal based process tool data is now available for some fraction of wafer processing tools in advanced factories, use of the data for optimizing tool performance relative to the devices (also referred to herein as integrated circuits or ICs) being produced has been limited. This limitation is due, at least in part, to a disconnect between how device performance data is represented relative to how process tool temporal data is represented. For example, data measurements on ICs are necessarily associated with a given batch of wafers (referred to as a lot), or a given wafer, or a given subset of ICs on the wafer. However, data measurements from process tool temporal data are represented as discrete operating conditions within the process tool at specific times during wafer processing. For example, if a factory process tool has an isolated chamber, then the chamber pressure might be recorded each millisecond while a given wafer remains in the process tool's chamber. In this example, the chamber pressure data for any given wafer would be recoded as a series of thousands of unique measurements. The difficulty associated with “merging” process tool temporal data to discrete data metrics has resulted in limited use of process tool temporal data as a means to optimize factory efficiency.
Many yield enhancement and factory efficiency improvement monitoring efforts today are focused on developing correlations between end-of-line functional test data that identify low yielding wafers and specific factory process tools used to make ICs. A goal of such an analysis is to identify individual process tool(s) suspected of causing the low yields, and to either remove it (them) from the factory processing flow until such time as yield engineers can be sure that the tool(s) is (are) operating “properly,” modify the tool performance if necessary as wafers are being produced. It is well known to use data mining to perform these tasks (these types of correlation activities also play an important part in developing yield ramps on new technologies). These types of correlation activities can be tedious and time-consuming because of: (a) the massive amount of data being generated; and (b) the time it takes to consolidate data for analysis. Sometimes, it is a matter of trial and error before a signal is found. In addition, such activities typically do not provide sufficient information to enable process tools to be made to perform better. As a result, users typically spend more time trying to identify problems than spending efforts contributing to the actual fixing or adjusting process tools after they are identified.
In light of the above, there is a need for method and apparatus for solving one or more of the above-identified problems.